Abstract
Background: Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fatigue, and, in severe case, recurrent thoughts of death. Despite its prevalence, reliable diagnostic biomarkers for MDD remain elusive. Identifying peripheral biomarkers for MDD is crucial for early diagnosis, timely intervention, and ultimately reducing the risk of suicide. Metabolic changes in peripheral blood mononuclear cells (PBMCs) have been observed in animal models of depression, suggesting that PBMC could serve as a valuable matrix for exploring potential peripheral biomarkers in MDD. Methods: We performed a transcriptomic analysis of PBMCs from patients with MDD and age- and sex-matched healthy controls (n = 20 per group). Results: Our analysis identified 270 differentially expressed genes in PBMCs from MDD patients compared to controls, which correlated with the Hamilton Depression Rating Scale scores. These genes are involved in several KEGG pathways, including the herpes simplex virus 1 infection pathway, NOD-like receptor signaling pathway, antigen processing and presentation, and glycerophospholipid metabolism-all of which are linked to various aspects of the immune response. Further machine learning analysis and quantitative real-time PCR (qPCR) validation identified three key genes-TRPV2, ZNF713, and CTSL-that effectively distinguish MDD patients from healthy controls. Conclusions: The immune dysregulation observed in PBMCs is closely related to the pathogenesis of MDD. The candidate biomarkers TRPV2, ZNF713, and CTSL, identified and validated through machine learning and qPCR, hold promise for the objective diagnosis of MDD. Trial Registration: Clinical Trial Registry identifier: ChiCTR2300076589.